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Maksymchuk N, Bustillo JR, Mathalon DH, Preda A, Miller RL, Calhoun VD. Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.15.599084. [PMID: 38948857 PMCID: PMC11212858 DOI: 10.1101/2024.06.15.599084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 schizophrenia patients and demographically matched 160 healthy controls. Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available brain networks between SZ patients and healthy controls (HC). These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN) and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to healthy control group. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. k-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that it appears healthy for a brain to primarily circulate through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. However, individuals with SZ seem to struggle with transiently attaining these more focused and structured connectivity patterns. Proposed ICE measure presents a novel framework for gaining deeper insights into understanding mechanisms of healthy and disease brain states and a substantial step forward in the developing advanced methods of diagnostics of mental health conditions.
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Affiliation(s)
- Natalia Maksymchuk
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Juan R. Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Daniel H. Mathalon
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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Ibanez A, Kringelbach ML, Deco G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn Sci 2024; 28:319-338. [PMID: 38246816 DOI: 10.1016/j.tics.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/15/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.
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Affiliation(s)
- Agustin Ibanez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile; Global Brain Health Institute (GBHI), University California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Morten L Kringelbach
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
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Rabus A, Curic D, Ivan VE, Esteves IM, Gruber AJ, Davidsen J. Changes in functional connectivity preserve scale-free neuronal and behavioral dynamics. Phys Rev E 2023; 108:L052301. [PMID: 38115411 DOI: 10.1103/physreve.108.l052301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/06/2023] [Indexed: 12/21/2023]
Abstract
Does the brain optimize itself for storage and transmission of information, and if so, how? The critical brain hypothesis is based in statistical physics and posits that the brain self-tunes its dynamics to a critical point or regime to maximize the repertoire of neuronal responses. Yet, the robustness of this regime, especially with respect to changes in the functional connectivity, remains an unsolved fundamental challenge. Here, we show that both scale-free neuronal dynamics and self-similar features of behavioral dynamics persist following significant changes in functional connectivity. Specifically, we find that the psychedelic compound ibogaine that is associated with an altered state of consciousness fundamentally alters the functional connectivity in the retrosplenial cortex of mice. Yet, the scale-free statistics of movement and of neuronal avalanches among behaviorally related neurons remain largely unaltered. This indicates that the propagation of information within biological neural networks is robust to changes in functional organization of subpopulations of neurons, opening up a new perspective on how the adaptive nature of functional networks may lead to optimality of information transmission in the brain.
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Affiliation(s)
- Anja Rabus
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Davor Curic
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Victorita E Ivan
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Ingrid M Esteves
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Aaron J Gruber
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Jörn Davidsen
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada T2N 4N1
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Okujeni S, Egert U. Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks. J Neurosci 2023; 43:2515-2526. [PMID: 36868860 PMCID: PMC10082461 DOI: 10.1523/jneurosci.1420-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/05/2023] Open
Abstract
Numerous studies suggest that biological neuronal networks self-organize toward a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro, which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent self-organization can tune inherently supercritical networks toward mesoscale criticality by creating a modular structure in neuronal networks.SIGNIFICANCE STATEMENT Critical recruitment dynamics in neuronal networks are considered optimal for information processing in the brain. However, it remains heavily debated how neuronal networks would self-organize criticality by detailed fine-tuning of connectivity, inhibition, and excitability. We provide experimental support for theoretical considerations that modularity tunes critical recruitment dynamics at the mesoscale level of interacting neuron clusters. This reconciles reports of supercritical recruitment dynamics in local neuron clusters with findings on criticality sampled at mesoscopic network scales. Intriguingly, altered mesoscale organization is a prominent aspect of various neuropathological diseases currently investigated in the framework of criticality. We therefore believe that our findings would also be of interest for clinical scientists searching to link the functional and anatomic signatures of such brain disorders.
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Affiliation(s)
- Samora Okujeni
- Laboratory for Biomicrotechnology, Department of Microsystems Engineering-Institut für Mikrosystemtechnik, Faculty of Engineering, University of Freiburg, 79110 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
| | - Ulrich Egert
- Laboratory for Biomicrotechnology, Department of Microsystems Engineering-Institut für Mikrosystemtechnik, Faculty of Engineering, University of Freiburg, 79110 Freiburg, Germany
- Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany
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Wang X, Chang Z, Wang R. Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia. Commun Biol 2023; 6:279. [PMID: 36932140 PMCID: PMC10023794 DOI: 10.1038/s42003-023-04637-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
Schizophrenia is a severe psychotic disorder characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional networks in schizophrenia patients and healthy controls, and we studied dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network (DMN) and control systems. In a machine-learning model, NSP-based features outperformed graph measures at predicting positive and negative symptoms. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.
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Affiliation(s)
- Xinrui Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Zhao Chang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
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Fosque LJ, Alipour A, Zare M, Williams-García RV, Beggs JM, Ortiz G. Quasicriticality explains variability of human neural dynamics across life span. Front Comput Neurosci 2022; 16:1037550. [PMID: 36532868 PMCID: PMC9747757 DOI: 10.3389/fncom.2022.1037550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/27/2022] [Indexed: 08/26/2023] Open
Abstract
Aging impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset-the largest aging cohort available-in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus. Examination of the data using this framework reveals interesting correlations with age and gender of test subjects. Using simulated data as verification, our results suggest a link between changes to brain connectivity due to aging and increased dynamical fluctuations of neuronal firing rates. Our findings suggest a platform to develop biomarkers of neurological health.
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Affiliation(s)
- Leandro J. Fosque
- Department of Physics, Indiana University, Bloomington, IN, United States
| | - Abolfazl Alipour
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | | | | | - John M. Beggs
- Department of Physics, Indiana University, Bloomington, IN, United States
| | - Gerardo Ortiz
- Department of Physics, Indiana University, Bloomington, IN, United States
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O'Byrne J, Jerbi K. How critical is brain criticality? Trends Neurosci 2022; 45:820-837. [PMID: 36096888 DOI: 10.1016/j.tins.2022.08.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.
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Affiliation(s)
- Jordan O'Byrne
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada; MILA (Quebec Artificial Intelligence Institute), Montreal, Quebec, Canada; UNIQUE Center (Quebec Neuro-AI Research Center), Montreal, Quebec, Canada.
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Kelty-Stephen DG, Mangalam M. Turing's cascade instability supports the coordination of the mind, brain, and behavior. Neurosci Biobehav Rev 2022; 141:104810. [PMID: 35932950 DOI: 10.1016/j.neubiorev.2022.104810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/09/2022] [Accepted: 08/01/2022] [Indexed: 10/16/2022]
Abstract
Turing inspired a computer metaphor of the mind and brain that has been handy and has spawned decades of empirical investigation, but he did much more and offered behavioral and cognitive sciences another metaphor-that of the cascade. The time has come to confront Turing's cascading instability, which suggests a geometrical framework driven by power laws and can be studied using multifractal formalism and multiscale probability density function analysis. Here, we review a rapidly growing body of scientific investigations revealing signatures of cascade instability and their consequences for a perceiving, acting, and thinking organism. We review work related to executive functioning (planning to act), postural control (bodily poise for turning plans into action), and effortful perception (action to gather information in a single modality and action to blend multimodal information). We also review findings on neuronal avalanches in the brain, specifically about neural participation in body-wide cascades. Turing's cascade instability blends the mind, brain, and behavior across space and time scales and provides an alternative to the dominant computer metaphor.
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Affiliation(s)
- Damian G Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, NY, USA.
| | - Madhur Mangalam
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, USA.
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E DO, V MS, S LV, E SY. Fractal Structure of Brain Electrical Activity of Patients With Mental Disorders. Front Physiol 2022; 13:905318. [PMID: 35923231 PMCID: PMC9340582 DOI: 10.3389/fphys.2022.905318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/23/2022] [Indexed: 11/19/2022] Open
Abstract
This work was aimed at a comparative analysis of the degree of multifractality of electroencephalographic time series obtained from a group of healthy subjects and from patients with mental disorders. We analyzed long-term records of patients with paranoid schizophrenia and patients with depression. To evaluate the properties of multifractal scaling of various electroencephalographic time series, the method of maximum modulus of the wavelet transform and multifractal analysis of fluctuations without a trend were used. The stability of the width and position of the singularity spectrum for each of the test groups was revealed, and a relationship was established between the correlation and anticorrelation dynamics of successive values of the electroencephalographic time series and the type of mental disorders. It was shown that the main differences between the multifractal properties of brain activity in normal and pathological conditions lie in the different width of the multifractality spectrum and its location associated with the correlated or anticorrelated dynamics of the values of successive time series. It was found that the schizophrenia group is characterized by a greater degree of multifractality compared to the depression group. Thus, the degree of multifractality can be included in a set of tests for differential diagnosis and research of mental disorders.
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Affiliation(s)
- Dick O. E
- Laboratory of Physiology of Reception, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
- *Correspondence: Dick O. E,
| | - Murav’eva S. V
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
| | - Lebedev V. S
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
| | - Shelepin Yu. E
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
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